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» Measuring High-Performance Computing with Real Applications
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KDD
2006
ACM
164views Data Mining» more  KDD 2006»
15 years 10 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
VIS
2007
IEEE
157views Visualization» more  VIS 2007»
15 years 11 months ago
Surface Extraction from Multi-Material Components for Metrology using Dual Energy CT
Abstract-- This paper describes a novel method for creating surface models of multi-material components using dual energy computed tomography (DECT). The application scenario is me...
Christoph Heinzl, Johann Kastner, Eduard Grölle...
IPPS
1995
IEEE
15 years 1 months ago
Characterizing parallel file-access patterns on a large-scale multiprocessor
High-performance parallel file systems are needed to satisfy tremendous I/O requirements of parallel scientific applications. The design of such high-performance parallel file ...
Apratim Purakayastha, Carla Schlatter Ellis, David...
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ICPR
2008
IEEE
15 years 4 months ago
Effective scene matching with local feature representatives
Scene matching measures the similarity of scenes in photos and is of central importance in applications where we have to properly organize large amount of digital photos by scene ...
Shugao Ma, Weiqiang Wang, Qingming Huang, Shuqiang...
CVPR
2005
IEEE
15 years 3 months ago
Jensen-Shannon Boosting Learning for Object Recognition
In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shan...
Xiangsheng Huang, Stan Z. Li, Yangsheng Wang